1. Field of the Invention
The present invention generally relates to providing location-aware (or location-dependent) services in a mobile environment. More specifically, a query indexing system and method that allow the user of this invention to periodically and incrementally locate all the moving objects inside the boundaries of continual range queries. “Incrementally” may be defined as skipping certain query evaluations by filtering out a subset of moving objects based on the relative movements from the last positions with respect to query boundaries.
2. Description of the Related Art
Location-aware (or location-dependent) services have become possible and emerging recently, thanks to the advances in mobile computing and location-sensing technologies, such as the global positioning system (GPS). These services can improve the quality of life by adding location-awareness to many objects of interest, such as humans, taxi cabs, ambulances, laptops, airplanes, trains and cargo ships. Basically, any object that moves can be equipped with location-awareness and monitored.
One of the most fundamental technical problems for providing location-aware services is to monitor and provide fast answers to continual range queries that locate the moving objects inside the query boundaries. With the results of continual range queries readily available, various kinds of location-aware services can be provided. For example, a taxi cab service company can dispatch a taxi cab to a customer at a specific location using the result from a continual range query such as: “Finding all the taxi cabs currently located within five blocks away from that specific location.” The range queries are termed “continual” because they are repeatedly evaluated in order to provide the most up-to-date answers as objects continuously to move around. Each range query specifies the boundaries of a region.
An efficient index is usually needed for monitoring numerous continual range queries over a large number of moving objects. There are generally two different indexing approaches. One is to index the moving objects, and the other is to index the range queries.
Various attempts have been proposed to build an index on the moving objects, such as the following:                “Indexing moving objects,” by P. K. Agarwal et al., in Proceedings of ACM Symposium on Principles of Database Systems, 2000;        “On indexing mobile objects,” by G. Kollios et al., in Proceedings of ACM Symposium on Principles of Database Systems, 1999;        “Novel approaches to the indexing of moving object trajectories,” by D. Pfoser et al., in Proceedings of Very Large Data Bases, 2000; and        “Indexing the positions of continuously moving objects,” by S. Saltenis et al., in Proceedings of ACM SIGMOD, 2000.        
However, because objects may continuously move in unpredictable speed and direction, it is very difficult to maintain an effective index on the moving objects. Changes in object locations demand updates to the object index, greatly degrading its performance. As a result, certain constraints are usually placed on the moving speed or direction of an object, significantly limiting the applicability of the object index.
In contrast, it is more effective to build an index on the range queries because continual range queries change less frequently. With query indexing, the problem of monitoring continual range queries becomes the following: Given a set of range queries and a set of moving objects, continually determine the set of objects that are located inside the boundaries of each range query.
The query index is used to quickly retrieve all the range queries that cover a given object. Periodically, all the range queries are reevaluated by identifying all the objects covered by each range query taking into account their latest locations. In order for the results to be useful, the time period between two consecutive reevaluations must be short. As a result, the time for a query reevaluation must be as brief as possible. Moreover, it is important that a query indexing method can take advantage of incremental changes in object locations because some of the objects may not have moved outside a query boundaries since the last reevaluation. This invention discloses an effective query indexing system and method for monitoring continual range queries over moving objects.
Query indexing was not used in the moving object environment until recently. In “Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects,” IEEE Transactions on Computers, 51:1124-1140, October 2002, S. Prabhakar et al. proposed a query indexing method using an R-tree. However, in order to avoid excessive location updates, a safe region for each mobile object was defined. Unfortunately, determining a safe region requires intensive computation. Moreover, a constraint is imposed on the maximum velocity of a moving object to compute the safe regions.
In “Efficient evaluation of continuous range queries on moving objects,” Proceedings of 13th International Conference on Database and Expert Systems Applications, 2002, D. V. Kalashnikov et al. proposed a grid cell-based query indexing method, which was shown to outperform an R-tree-based query index. The monitoring area is partitioned into non-overlapping grid cells. Each cell maintains two lists: full and partial. The full list stores the IDs of queries that completely cover the cell, while the partial list maintains the IDs of queries that partially intersect with the cell.
However, the need to use the partial lists has a significant drawback. The fact that an object is inside a cell does not imply that it is inside the boundaries of a query stored in the partial list of the cell. This forces continual comparisons of object locations against the boundaries of queries. As a result, it cannot take advantage of the incremental changes in object locations. Even if an object has not moved outside a cell, boundary comparisons against all the queries stored in a partial list are still needed.
As a result, a need has been recognized to have a better and more effective query indexing method that:
(1) does not impose any limit on the moving speed or direction of an object, and
(2) can take advantage of incremental changes in object locations.
Those skilled in the art will appreciate that various query indexing methods have been proposed to efficiently matching events in the context of predicate matching (e.g., E. Hanson et al., “Selection predicate indexing for active databases using interval skip lists,” Information Systems, 21(3):269-298, 1996) and pub/sub (e.g., M. K. Aguilera et al., “Matching events in a content-based subscription system,” Proceedings of Symposium on Principles of Distributed Computing, 1999; F. Fabret et al, “Filtering algorithms and implementation for very fast publish/subscribe systems,” Proceedings of the ACM SIGMOD, 2001).
However, these query indexing methods are mostly based on equality predicates, not range predicates. Thus, they are not generally applicable for the evaluation of continual range queries over moving objects.
Those skilled in the art will also appreciate that, although range queries can be treated as spatial objects such as rectangles, traditional spatial indexing methods, such as R-trees (e.g., A. Guttman, “R-trees: A dynamic index structure for spatial searching,” Proceedings of ACM SIGMOD, 1984; V. Gaede et al., “Multidimensional access methods,” ACM Computing Surveys, 30(2):170-231, 1998), are not effective for monitoring moving objects because they are mostly disk-based indexing methods.
They are generally too slow to be effective for monitoring continual range queries over a large number of moving objects. Moreover, the performance of an R-tree quickly degenerates when the ranges of continual queries start to overlap one another (V. Gaede et al., “Multidimensional access methods,” ACM Computing Surveys, 30(2):170-231, 1998; E. Hanson et al., “Selection predicate indexing for active databases using interval skip lists,” Information Systems, 21(3):269-298, 1996).